Analytics: An Executive Primer

Analytics offer the promise of improved business results through data. By accessing data from internal sources – and often enriching it with data from external sources – then applying algorithms and statistical methods, business insights come to light. Done right, the insights are presented through a user-friendly interface with the ability to drill down and perform “what if” analysis to encourage further mining and exploration.

There are multiple types of analytic solutions, ranging from the simple to the very complex. It’s important to choose the right solution for the business need, as business benefits and associated costs increase with complexity.

In this executive primer, we’ll touch on each of the four major analytic categories, starting with the simplest type. As always, focus on the business issues and questions at hand. The approach and technical methods are then selected to satisfy those needs.

Describe: Descriptive Analytics

Descriptive analytics tools and practices help identify trouble spots, focus attention on the problems that need to be solved, and allow you to assess the impact of your actions. They use data to show what you’ve done, where you’ve been, and, if timely, where you are now. Often these are simple aggregations, such as counts and percentages, drawn directly from production data systems. When someone speaks of a Business Intelligence or BI solution, it is often synonymous with descriptive analytics.

Some examples of questions descriptive analytics can answer include:

Are we meeting our sales targets?

In which products are sales growing, staying flat, or declining across periods?

What is our lead conversion rate?

How quickly are we using our inventory?

Understand: Diagnostic Analytics

Diagnostic analytics focus on comparing and contrasting groups to find meaningful differences that lead to root causes. This type of analysis uses data and statistical methods to understand how groups differ (e.g., people, machines, inventory, processes). It often involves analytical tools that create meaningful groups (such as in customer segmentation), as well as techniques that allow for statistical comparisons among them.

Questions applicable to diagnostic analyses include:

Who is buying our products? Are they similar in ways that will help us identify new customers?

Is this revenue loss due to fraud?

How do individual staff contribute to productivity? Revenue?

Predict: Predictive Analytics

Predictive analytics use data from the past to predict what will happen in the future. They apply statistical models to existing data to generate a score or classification that represents the likelihood of a specific future event taking place. For example, your credit score is a prediction of the likelihood you will pay future bills based on your past bill-paying behavior.

Questions answered by predictive analytics usually focus on individuals, although these responses can be aggregated to get a larger picture:

Which customers are more likely to buy again?

Which customers are more likely to buy another one of our products?

Which leads are likely to become paying customers?

How would a change in price affect total revenues?

Optimize: Prescriptive Analytics

Prescriptive analytics apply statistical and mathematical tools to data to make recommendations. These tools leverage patterns and relationships in data to evaluate complex combinations of options and potential outcomes to find the one that best meets all requirements. Such processes are vital in the areas of supply chain management, pricing optimization, and demand forecasting.

Prescriptive analytics use data, analyses, and decision science to answer questions such as:

What is the next best product to offer to each customer?

What is the best way to market this product?

How frequently should we order inventory? How much at a time?

What is the optimal price for this product?

Key Takeaway

Analytics can help increase sales, reduce cost and risk, and optimize operations. The right type of analytic solution must be selected for the job at hand. Starting with the business issues or questions is paramount. Approach and technology are just means to satisfy the business needs.

In future posts, we’ll cover the other critical ingredients needed to obtain real results from analytics. Those posts will speak to data (sources, quality, etc.), user tools, technical infrastructure, people and organization, and governance.

About RedMane

RedMane provides software solutions and systems integration services that address complex, real-world challenges in human services, healthcare, and the commercial sector. We are a problem-solving company. Technology is just one of our tools.

To speak with a RedMane expert about your needs, email info@redmane.com or call 773-331-0001 today.

About Tim Connell

Tim Connell is a data scientist and solution architect at RedMane Technology. He’s been performing data analysis and implementing solutions for over 25 years. Tim’s work spans commercial firms and the public sector. He holds a Ph.D. from the University of Wisconsin.

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